```
simulate(model, input=NULL, input0=NULL,
sampleT=100, start. = NULL, freq = NULL,
noise=NULL, noise.model=NULL,sd=1, SIGMA=NULL,
rng=NULL, compiled=.DSECOMPILED)
```

model

An object of class TSmodel or TSestModel.

input

Data for the exogenous variable if specified in the model.

sampleT

The length of the sample to simulate.

start.

start. date for resulting data.

freq

freq for resulting data.

y0, input0

Lagged values prior to t=1 for y and u, in reverse order so y0[1,] and
input0[1,]correspond to t=0. These arguments are not implemented for state
space models. If not specified initial values are set to zero.

noise

Noise can be supplied. Otherwise it will be generated.
If supplied it should be a list as described below under returned value.

SIGMA

The covariance of the noise process. If this is specified then sd is ignored.
A vector or scalar is treated as a diagonal matrix. For an object of class
TSestModel, if neither SIGMA nor sd are specified, then SIGMA is set to
the estimated covariance (mod

sd

The standard deviation of the noise. This can be a vector.

noise.model

A TSmodel to be used for generating noise (not yet supported by SS methods).

rng

The random number generator and seed can be specified to
regenerate a simulation.

seed

The seed can be specified to regenerate a simulation.
(rng is better.)

compiled

- The value returned is an object of class TSdata which can be supplied as an argument to estimation routines. (See TSdata). In addition to the usual elements (see the description of a TSdata object) there are some additional elements: $model- the generating model, $rng - the initial RNG and seed, $version - the version of S used (random number generators may vary) $SIGMA as specified $sd as specified $noise - a list of $e, $w and $w0 - the noise processes. $w0 is w for t=0 in the state space model and prior lags in ARMA models. For VAR models B has no lags so w0 has no effect. $state - the state variable for state space models.

`TSmodel`

,
`TSdata`

if(is.R()) data("eg1.DSE.data.diff", package="dse1") model <- est.VARX.ls(eg1.DSE.data.diff) z <- simulate(model)